Predictive Analytics Demystified

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Many organizations have made big investments in Business Intelligence, deploying sophisticated reporting systems and performance dashboards. Yet for most companies, translating "interesting insights" into quantifiable business benefits is more the exception than the rule. Predictive analytics can be the next logical step in the evolution toward achieving dramatic improvements to the bottom line. This presentation, Eric Zankman, 20 year expert in analytics, demystifies predictive analytics by explaining what predictive models are, how to develop them, and how to apply them within a customer management framework to create measurable Return on Investment (ROI).

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Predictive Analytics Demystified

  1. 1. 1Helping Companies Learn From the Past, Manage thePresent and Shape the Futurewww.senturus.comPredictive Analytics Demystified
  2. 2. 2This slide deck is part of a recorded webinar.To view the FREE recording of this entirepresentation and download the slide deck, go tohttp://info.senturus.com/2013-05-30-Predictive-Analytics-Demystified.htmlThe Senturus library has over 90 other recorded webinars,whitepapers, and demonstrations on assorted Cognos and BI topicswhich may interest you.Go to the recorded resources
  3. 3. 3Agenda• Introduction• What are Predictive Models?• Why Build Predictive Models?• Comprehensive Analytics Solution• Reaping the Benefits of Models• Live Demonstration: IBM SPSS Modeler• Questions and Answers
  4. 4. 4Presenters TodayGreg HerreraCo-Founder/CEOSenturusArik KillionClient Technical ProfessionalIBM Business Analytics, SPSSEric ZankmanAnalytics and BI ConsultantSenturus, Inc20-year track record of• applying data mining• predictive modeling• customer segmentation,• experimental design andoptimization
  5. 5. 5Who is Senturus ?• Consulting firm specializing in Corporate PerformanceManagement– Business Intelligence– Predictive Analytics– San Francisco Business Times Hall of Fame -- Fourconsecutive years in Fast 100 list of fastest-growingprivate companies in the Bay Area• Experience– 13-year focus on performance management– More than 1,200 projects for 650+ clients• People– Business depth combined with technical expertise.Former CFOs, CIOs, Controllers, Directors...– DBAs with MBAswww.Senturus.com 888.601.6010 info@senturus.com
  6. 6. 6A few of our 650+ Clients
  7. 7. 7What are Predictive Models?Empirically-derived algorithms used to predict future outcomesIn the context of customer analytics, a model:• predicts future customer actions• combines individual attributes that are strong predictors• produces an assessment score for each customer
  8. 8. 8Uses of Predictive Models• Direct Marketing• Underwriting• Usage Stimulation• Cross-sell / Up-sell• Retention / Churn• Customer ValueAcquireGrowRetainPredictiveCustomer AnalyticsPredictiveThreat & Fraud AnalyticsMonitorDetectControlPredictiveOperational AnalyticsManageMaintainMaximize• Risk Management• Credit PolicyDecisions• Channel Preference• PortfolioManagement• Fraud Detection• Collections• Write-offs /Recoveries
  9. 9. 9Predictive Modeling Timeline:Data snapshots from multiple points-in-time used to simulate forecastForecastObservation Period Performance PeriodObservationPointFuture Customer BehaviorPast Customer Behavior
  10. 10. 10Model Objective FunctionSpecifies the Customer Behavior to be Predicted• Who is Included in the Customer Sample?– Define all Inclusions and Exclusions• How is Customer Performance Defined?– Define “desirable” customers– Define “undesirable” customers– Define “indeterminate” customers• How Long is the Performance Period?– Define the Observation and Performance Dates
  11. 11. 11Example Objective Function for ChurnCustomer Performance(Pertains only performance period)– Good: Non-churn– Bad: Voluntary Churn– Indeterminate: Involuntary Churn(customer terminated for non-payment)ForecastObservation Period =prior to 1/1/2013Performance Period =1/1/2013 to 4/30/2013ObservationPoint =1/1/2013Future Customer BehaviorPast Customer BehaviorCustomer Sample– Include current customersas of 1/1/2013– Exclude customers with nopurchase/payment activityin the 24 months prior to 1/1/2013
  12. 12. 12Example Predictive Model for ChurnAccount Age ScoreLess than 1 year 411 to less than 2 years 583 to less than 5 years 975 years or more 102Average Balancein the Last 6 Months$0 - $75 80$75.01 - $120 49$120.01 or more 41Roaming Chargesin the Last 3 Months$0 - $20 80$20.01 - $40 68$40.01 - $75 40$75.01 or more 29Dropped Callsin the Last Month0 1101 572 413 or more 30Number of ScorePremium Services0 351 - 2 573 or more 69Number of Customer ServiceCalls in the Last 3 Months0 851 - 2 383 - 4 295 or more 18Number of Customer Disputesin the Last 12 Months0 1321 - 2 973 or more 40Total Scorefor example customer= 421
  13. 13. 13Why Build Predictive Models?Predictive models harness the knowledgewithin your data about customer behaviorso you can treat different customersdifferently, tailoring the right treatmentsand offers to the right customers, therebyimproving customer strategies andincreasing profitability
  14. 14. 14Evaluating Model Performance0%10%20%30%40%50%60%70%80%90%100%ACTUALCustomerBadRateatTIME1FORECASTED Customer Score (in deciles) at TIME 0• Customers were scored at TIME 0 andrank-ordered by Customer Score• Bars show actual bad rate at TIME 1Low Score High Score
  15. 15. 15Translating Insights into Action0%10%20%30%40%50%60%70%80%90%100%ACTUALCustomerBadRateatTIME1FORECASTED Customer Score (in deciles) at TIME 0TreatdifferentcustomersdifferentlyLow Score High Score
  16. 16. 16This slide deck is part of a recorded webinar.To view the FREE recording of this entirepresentation and download the slide deck, go tohttp://info.senturus.com/2013-05-30-Predictive-Analytics-Demystified.htmlThe Senturus library has over 90 other recordedwebinars, whitepapers, and demonstrations on assorted Cognos andBI topics which may interest you.Go to the recorded resources
  17. 17. 17Predictive Models: Part of a ComprehensiveAnalytics SolutionCustomerData MartCustomerSegmentationChampion/ChallengerStrategy TestsModel, Segmentation, andStrategy ExecutionNew ChampionStrategyStrategy TestEvaluationPredictiveModelsContinuousLearning andImprovement
  18. 18. 18Solution – Customer Data MartStore the right data for predictive modeling and analysisCustomer-level DataSummary BillingDataDetailed TransactionDataPredictive ModelDevelopmentStrategy TestEvaluationOngoing Analysisand Model ValidationCustomer DataMart
  19. 19. 19Solution – Predictive ModelsApply Modeling Methodology• Define business goals• Specify model objective function• Design/build modeling database• Partition modeling data• Derive potential predictors• Analyze predictor strength• Perform sub-population analysis• Build model algorithms• Evaluate model performance• Deploy model
  20. 20. 20Solution – Customer SegmentationCreate customer groups to enable differentiated strategiesApply model scores and other criteria to segment customersAll Customers
  21. 21. 21Solution – Champion/Challenger TestsEmploy test-and-learn methodologies to evolve strategies• Develop a “champion”strategy and “challenger”strategies for each segment• Execute strategy tests andanalyze results after adefined test period• Perform model validation(controlling for treatment)• Deploy new championstrategy with quantifiablebusiness improvements• Create new round ofpromising challengersModel Score Low Medium HighChampion(85%)Treatment A1 Treatment B1 Treatment C1Challenger 1(5%)Treatment A2 Treatment A1 Treatment C2Challenger 2(5%)Treatment B1 Treatment C1 Treatment B1Challenger 3(5%)Treatment B2 Treatment B2 Treatment B2
  22. 22. 22Solution – Test and Learn Feedback LoopEstablish culture and capabilities for continuous strategy improvement
  23. 23. 23Reaping the Benefits: Churn ManagementCustomerSegmentationChampion/ChallengerStrategy TestsModel, Segmentationand Strategy ExecutionStrategy TestEvaluationNew ChampionStrategyValue and ChurnPredictive ModelsCustomerDataMartContinuousLearningandImprovementSolution Develop predictive models for customer value and churn Identify customers with high value and/or high churn propensity for tailored treatments(e.g., special retention campaigns, VIP service, liberal fee-reversal policies) Conduct champion/challenger tests to identify the best treatments for each segment Implement new champion and develop next set of challengersPerformance Measures Customer value Churn rates Average Revenue per User(ARPU) Customer tenure Average number of products Retention costsChallenges Rising churn rates Declining revenue per customer High costs to acquire newcustomers
  24. 24. 24Predictive Analytics - Summary• Predictive analytics can greatly improve profitabilitywhen part of a comprehensive solution• A well-designed data mart is the first step towardeffective predictive analytics• Organizations must be committed to ongoing strategytesting to maximize their benefits
  25. 25. 25This slide deck is part of a recorded webinar.To view the FREE recording of this entirepresentation and download the slide deck, go tohttp://info.senturus.com/2013-05-30-Predictive-Analytics-Demystified.htmlThe Senturus library has over 90 other recorded webinars,whitepapers, and demonstrations on assorted Cognos and BI topicswhich may interest you.Go to the recorded resources
  26. 26. 26DEMONSTRATION: IBM SPSS MODELER
  27. 27. 0.0%5.0%10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%Attrition Rate
  28. 28. • Through gut instinct and hypothesis – this analyst concludessubscribers that are Single and pay by Credit Card have thelargest attrition• Another analyst might arrive at a completely different conclusion
  29. 29. 33Other ResourcesIn-Person IBM/SPSS EventsIBM Business Analytics Summit June 13, 2013 San Francisco• complimentary, one-day event• Emphasis on SPSS with many breakout sessions focused on predictive analytics• Click below to go to IBMs website and learn more about the event and register:http://www.senturus.com/continue.php?link=aHR0cHM6Ly93d3cuaWJtLmNvbS9ldmVudHMvd3dlL2dycC9ncnAwMDQubnNmL2FnZW5kYT9vcGVuZm9ybSZzZW1pbmFyPUIyRktSM0VTJmxvY2FsZT1lbl9VUyZTX1RBQ1Q9QlBfU2VudHVydXM=SPSS Modeling Workshops• San Francisco, CA June 18• Costa Mesa, CA June 19• Minneapolis, MN June 20Dates being finalized for workshops in July – December throughout the U.S. and CanadaEmail to follow with details.
  30. 30. 34Other Resources: IBM Analytic Answers• Prepackaged solutions• Cloud basedAnalytic Answers for Student Retentionhttp://public.dhe.ibm.com/common/ssi/ecm/en/ytd03292usen/YTD03292USEN.PDFAnalytic Answers for Prioritized Collectionshttp://public.dhe.ibm.com/common/ssi/ecm/en/ytd03291usen/YTD03291USEN.PDFAnalytic Answers for Retail Purchase Analysis and Offer Targetinghttp://public.dhe.ibm.com/common/ssi/ecm/en/ytd03289usen/YTD03289USEN.PDFAnalytic Answers for Insurance Renewalshttp://public.dhe.ibm.com/common/ssi/ecm/en/ytd03290usen/YTD03290USEN.PDF
  31. 31. 35This slide deck is part of a recorded webinar.To view the FREE recording of this entirepresentation and download the slide deck, go tohttp://info.senturus.com/2013-05-30-Predictive-Analytics-Demystified.htmlThe Senturus library has over 90 other recordedwebinars, whitepapers, and demonstrations on assorted Cognos andBI topics which may interest you.Go to the recorded resources
  32. 32. 36Helping Companies Learn From the Past, Manage thePresent and Shape the Futurewww.senturus.com888-601-6010info@senturus.comCopyright 2013 by Senturus, Inc. This entire presentation iscopyrighted and may not be reused or distributed without the written consent ofSenturus, Inc.
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